Overview

Dataset statistics

Number of variables14
Number of observations7979
Missing cells1689
Missing cells (%)1.5%
Total size in memory872.8 KiB
Average record size in memory112.0 B

Variable types

Text8
Numeric6

Dataset

DescriptionA dataset from the WAMEX database.
URLhttps://www.dmp.wa.gov.au/WAMEX-Minerals-Exploration-1476.aspx

Alerts

Unnamed: 0 has constant value ""Constant
CollarDip has constant value ""Constant
CollarAzimuth has constant value ""Constant
DEPTH has 93 (1.2%) missing valuesMissing
Surveyed has 896 (11.2%) missing valuesMissing
CollarAzimuth has 7923 (99.3%) zerosZeros

Reproduction

Analysis started2023-07-19 23:00:59.822904
Analysis finished2023-07-19 23:01:00.567357
Duration0.74 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing54
Missing (%)0.7%
Memory size62.5 KiB
2023-07-20T07:01:00.658587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7925
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD
ValueCountFrequency (%)
d 7925
100.0%
2023-07-20T07:01:01.332580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 7925
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7925
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7925
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 7925
100.0%

HOLEID
Text

Distinct7925
Distinct (%)100.0%
Missing54
Missing (%)0.7%
Memory size62.5 KiB
2023-07-20T07:01:02.365217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.048328076
Min length2

Characters and Unicode

Total characters47933
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7925 ?
Unique (%)100.0%

Sample

1st rowCB0001
2nd rowCB0002
3rd rowCB0003
4th rowCB0004
5th rowCB0005
ValueCountFrequency (%)
cc1911 1
 
< 0.1%
cb0030 1
 
< 0.1%
cb0004 1
 
< 0.1%
cb0005 1
 
< 0.1%
cb0006 1
 
< 0.1%
cb0007 1
 
< 0.1%
cb0008 1
 
< 0.1%
cb0009 1
 
< 0.1%
cb0010 1
 
< 0.1%
cb0011 1
 
< 0.1%
Other values (7916) 7916
99.9%
2023-07-20T07:01:03.834535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 11730
24.5%
0 5632
11.7%
1 4622
 
9.6%
2 4333
 
9.0%
3 3511
 
7.3%
B 3464
 
7.2%
4 2377
 
5.0%
5 2274
 
4.7%
6 2258
 
4.7%
7 2245
 
4.7%
Other values (15) 5487
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31609
65.9%
Uppercase Letter 16322
34.1%
Space Separator 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 11730
71.9%
B 3464
 
21.2%
W 313
 
1.9%
K 197
 
1.2%
D 183
 
1.1%
G 156
 
1.0%
F 140
 
0.9%
S 56
 
0.3%
P 55
 
0.3%
E 17
 
0.1%
Other values (3) 11
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 5632
17.8%
1 4622
14.6%
2 4333
13.7%
3 3511
11.1%
4 2377
7.5%
5 2274
7.2%
6 2258
7.1%
7 2245
 
7.1%
8 2191
 
6.9%
9 2166
 
6.9%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31611
65.9%
Latin 16322
34.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 11730
71.9%
B 3464
 
21.2%
W 313
 
1.9%
K 197
 
1.2%
D 183
 
1.1%
G 156
 
1.0%
F 140
 
0.9%
S 56
 
0.3%
P 55
 
0.3%
E 17
 
0.1%
Other values (3) 11
 
0.1%
Common
ValueCountFrequency (%)
0 5632
17.8%
1 4622
14.6%
2 4333
13.7%
3 3511
11.1%
4 2377
7.5%
5 2274
7.2%
6 2258
7.1%
7 2245
 
7.1%
8 2191
 
6.9%
9 2166
 
6.9%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 11730
24.5%
0 5632
11.7%
1 4622
 
9.6%
2 4333
 
9.0%
3 3511
 
7.3%
B 3464
 
7.2%
4 2377
 
5.0%
5 2274
 
4.7%
6 2258
 
4.7%
7 2245
 
4.7%
Other values (15) 5487
11.4%

DEPTH
Real number (ℝ)

MISSING 

Distinct255
Distinct (%)3.2%
Missing93
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean41.84958154
Minimum3
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-07-20T07:01:04.286864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q124
median36
Q354
95-th percentile84
Maximum144
Range141
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.16511659
Coefficient of variation (CV)0.5057426099
Kurtosis0.7370953004
Mean41.84958154
Median Absolute Deviation (MAD)12
Skewness0.9329416516
Sum330025.8
Variance447.9621603
MonotonicityNot monotonic
2023-07-20T07:01:04.748610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 688
 
8.6%
30 561
 
7.0%
36 494
 
6.2%
42 441
 
5.5%
48 409
 
5.1%
54 388
 
4.9%
22 361
 
4.5%
60 305
 
3.8%
18 300
 
3.8%
66 245
 
3.1%
Other values (245) 3694
46.3%
ValueCountFrequency (%)
3 1
 
< 0.1%
3.5 1
 
< 0.1%
4 4
0.1%
5 3
< 0.1%
5.7 1
 
< 0.1%
ValueCountFrequency (%)
144 1
< 0.1%
142 1
< 0.1%
140 1
< 0.1%
137 1
< 0.1%
132 2
< 0.1%

RL
Real number (ℝ)

Distinct145
Distinct (%)1.8%
Missing54
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean440.0134167
Minimum401
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-07-20T07:01:05.177449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum401
5-th percentile420
Q1429
median435
Q3446
95-th percentile480
Maximum515
Range114
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.52758655
Coefficient of variation (CV)0.03983420934
Kurtosis1.828523213
Mean440.0134167
Median Absolute Deviation (MAD)7
Skewness1.349151125
Sum3487106.327
Variance307.2162903
MonotonicityNot monotonic
2023-07-20T07:01:05.580803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
432 357
 
4.5%
433 328
 
4.1%
431 318
 
4.0%
435 313
 
3.9%
434 310
 
3.9%
430 299
 
3.7%
436 285
 
3.6%
437 270
 
3.4%
429 267
 
3.3%
438 239
 
3.0%
Other values (135) 4939
61.9%
ValueCountFrequency (%)
401 1
 
< 0.1%
403 1
 
< 0.1%
404 3
< 0.1%
405 2
< 0.1%
406 2
< 0.1%
ValueCountFrequency (%)
515 3
< 0.1%
514 1
 
< 0.1%
512 3
< 0.1%
511 2
< 0.1%
510 2
< 0.1%

Method
Text

Distinct3
Distinct (%)< 0.1%
Missing54
Missing (%)0.7%
Memory size62.5 KiB
2023-07-20T07:01:05.933944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.124416404
Min length2

Characters and Unicode

Total characters16836
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRC
2nd rowRC
3rd rowRC
4th rowRC
5th rowRC
ValueCountFrequency (%)
rc 7702
97.2%
diamond 180
 
2.3%
bulk 43
 
0.5%
2023-07-20T07:01:06.698816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 7702
45.7%
C 7702
45.7%
D 180
 
1.1%
i 180
 
1.1%
a 180
 
1.1%
m 180
 
1.1%
o 180
 
1.1%
n 180
 
1.1%
d 180
 
1.1%
B 43
 
0.3%
Other values (3) 129
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15627
92.8%
Lowercase Letter 1209
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 180
14.9%
a 180
14.9%
m 180
14.9%
o 180
14.9%
n 180
14.9%
d 180
14.9%
u 43
 
3.6%
l 43
 
3.6%
k 43
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
R 7702
49.3%
C 7702
49.3%
D 180
 
1.2%
B 43
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 16836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 7702
45.7%
C 7702
45.7%
D 180
 
1.1%
i 180
 
1.1%
a 180
 
1.1%
m 180
 
1.1%
o 180
 
1.1%
n 180
 
1.1%
d 180
 
1.1%
B 43
 
0.3%
Other values (3) 129
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 7702
45.7%
C 7702
45.7%
D 180
 
1.1%
i 180
 
1.1%
a 180
 
1.1%
m 180
 
1.1%
o 180
 
1.1%
n 180
 
1.1%
d 180
 
1.1%
B 43
 
0.3%
Other values (3) 129
 
0.8%

EAST
Real number (ℝ)

Distinct4440
Distinct (%)56.0%
Missing54
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean763331.5736
Minimum634394
Maximum802413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-07-20T07:01:07.172668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum634394
5-th percentile722393.2
Q1745406
median767199
Q3789600
95-th percentile799395.8
Maximum802413
Range168019
Interquartile range (IQR)44194

Descriptive statistics

Standard deviation30715.72598
Coefficient of variation (CV)0.04023903509
Kurtosis2.484835593
Mean763331.5736
Median Absolute Deviation (MAD)22202
Skewness-1.216928864
Sum6049402721
Variance943455822.2
MonotonicityNot monotonic
2023-07-20T07:01:07.916457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795200 19
 
0.2%
746411 18
 
0.2%
794399 17
 
0.2%
785595 17
 
0.2%
784798 16
 
0.2%
746436 15
 
0.2%
794799 15
 
0.2%
746399 15
 
0.2%
783198 15
 
0.2%
746361 15
 
0.2%
Other values (4430) 7763
97.3%
(Missing) 54
 
0.7%
ValueCountFrequency (%)
634394 1
 
< 0.1%
634397 2
< 0.1%
634400 3
< 0.1%
634401 2
< 0.1%
634402 1
 
< 0.1%
ValueCountFrequency (%)
802413 2
< 0.1%
802411 1
< 0.1%
802410 1
< 0.1%
802404 2
< 0.1%
802402 1
< 0.1%

NORTH
Real number (ℝ)

Distinct7864
Distinct (%)99.2%
Missing54
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean7525953.295
Minimum7510749.19
Maximum7559646.655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-07-20T07:01:08.426033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7510749.19
5-th percentile7517701.453
Q17520811.49
median7524800.59
Q37528961.934
95-th percentile7535604.751
Maximum7559646.655
Range48897.465
Interquartile range (IQR)8150.444

Descriptive statistics

Standard deviation6960.370906
Coefficient of variation (CV)0.0009248490701
Kurtosis5.25607066
Mean7525953.295
Median Absolute Deviation (MAD)4098.352
Skewness1.719501262
Sum5.964317986 × 1010
Variance48446763.15
MonotonicityNot monotonic
2023-07-20T07:01:08.930986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7528389 3
 
< 0.1%
7535000 3
 
< 0.1%
7527795 2
 
< 0.1%
7526797 2
 
< 0.1%
7529001 2
 
< 0.1%
7533604 2
 
< 0.1%
7532010 2
 
< 0.1%
7532800 2
 
< 0.1%
7557600 2
 
< 0.1%
7558400 2
 
< 0.1%
Other values (7854) 7903
99.0%
(Missing) 54
 
0.7%
ValueCountFrequency (%)
7510749.19 2
< 0.1%
7510754.81 2
< 0.1%
7512993.182 1
< 0.1%
7512996.72 2
< 0.1%
7512996.74 1
< 0.1%
ValueCountFrequency (%)
7559646.655 1
< 0.1%
7559603.328 1
< 0.1%
7559581.981 1
< 0.1%
7559558.467 1
< 0.1%
7559545.616 1
< 0.1%

CollarDip
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean-90
Minimum-90
Maximum-90
Zeros0
Zeros (%)0.0%
Negative7923
Negative (%)99.3%
Memory size62.5 KiB
2023-07-20T07:01:09.190811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-90
5-th percentile-90
Q1-90
median-90
Q3-90
95-th percentile-90
Maximum-90
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)-0
Kurtosis0
Mean-90
Median Absolute Deviation (MAD)0
Skewness0
Sum-713070
Variance0
MonotonicityIncreasing
2023-07-20T07:01:09.483680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-90 7923
99.3%
(Missing) 56
 
0.7%
ValueCountFrequency (%)
-90 7923
99.3%
ValueCountFrequency (%)
-90 7923
99.3%

CollarAzimuth
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros7923
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size62.5 KiB
2023-07-20T07:01:09.835398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-20T07:01:10.180391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 7923
99.3%
(Missing) 56
 
0.7%
ValueCountFrequency (%)
0 7923
99.3%
ValueCountFrequency (%)
0 7923
99.3%

RigID
Text

Distinct19
Distinct (%)0.2%
Missing58
Missing (%)0.7%
Memory size62.5 KiB
2023-07-20T07:01:10.816718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.457770484
Min length3

Characters and Unicode

Total characters35310
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDD1
2nd rowDD1
3rd rowDD1
4th rowDD1
5th rowDD1
ValueCountFrequency (%)
mak6 1350
17.0%
rcd250 1132
14.3%
std4 1034
13.1%
mak5 1015
12.8%
mak1 722
9.1%
rcd100 653
8.2%
mak3 579
7.3%
std1 578
7.3%
std8 188
 
2.4%
arr1 165
 
2.1%
Other values (9) 505
 
6.4%
2023-07-20T07:01:11.980699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3917
11.1%
K 3908
11.1%
M 3749
10.6%
D 3720
10.5%
0 2655
 
7.5%
5 2347
 
6.6%
1 2185
 
6.2%
R 2115
 
6.0%
C 1916
 
5.4%
S 1800
 
5.1%
Other values (12) 6998
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23533
66.6%
Decimal Number 11777
33.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3917
16.6%
K 3908
16.6%
M 3749
15.9%
D 3720
15.8%
R 2115
9.0%
C 1916
8.1%
S 1800
7.6%
T 1800
7.6%
N 254
 
1.1%
G 159
 
0.7%
Other values (4) 195
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 2655
22.5%
5 2347
19.9%
1 2185
18.6%
6 1372
11.6%
2 1258
10.7%
4 1034
 
8.8%
3 579
 
4.9%
8 347
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 23533
66.6%
Common 11777
33.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3917
16.6%
K 3908
16.6%
M 3749
15.9%
D 3720
15.8%
R 2115
9.0%
C 1916
8.1%
S 1800
7.6%
T 1800
7.6%
N 254
 
1.1%
G 159
 
0.7%
Other values (4) 195
 
0.8%
Common
ValueCountFrequency (%)
0 2655
22.5%
5 2347
19.9%
1 2185
18.6%
6 1372
11.6%
2 1258
10.7%
4 1034
 
8.8%
3 579
 
4.9%
8 347
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3917
11.1%
K 3908
11.1%
M 3749
10.6%
D 3720
10.5%
0 2655
 
7.5%
5 2347
 
6.6%
1 2185
 
6.2%
R 2115
 
6.0%
C 1916
 
5.4%
S 1800
 
5.1%
Other values (12) 6998
19.8%

TENEMENTID
Text

Distinct11
Distinct (%)0.1%
Missing55
Missing (%)0.7%
Memory size62.5 KiB
2023-07-20T07:01:12.473903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.094144372
Min length7

Characters and Unicode

Total characters56214
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE45/2499
2nd rowE45/2499
3rd rowE45/2499
4th rowE45/2499
5th rowE45/2499
ValueCountFrequency (%)
e46/590 2991
37.7%
e46/610 1810
22.8%
e46/566 1790
22.6%
e46/612 554
 
7.0%
e45/2499 416
 
5.2%
e47/1016 116
 
1.5%
e45/2498 108
 
1.4%
e45/2593 87
 
1.1%
e46/611 33
 
0.4%
e45/2497 15
 
0.2%
2023-07-20T07:01:13.362231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 13275
23.6%
4 8463
15.1%
E 7924
14.1%
/ 7924
14.1%
5 5502
9.8%
0 4917
 
8.7%
9 4033
 
7.2%
1 2662
 
4.7%
2 1188
 
2.1%
7 131
 
0.2%
Other values (2) 195
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40366
71.8%
Uppercase Letter 7924
 
14.1%
Other Punctuation 7924
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 13275
32.9%
4 8463
21.0%
5 5502
13.6%
0 4917
 
12.2%
9 4033
 
10.0%
1 2662
 
6.6%
2 1188
 
2.9%
7 131
 
0.3%
8 108
 
0.3%
3 87
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
E 7924
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 7924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48290
85.9%
Latin 7924
 
14.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 13275
27.5%
4 8463
17.5%
/ 7924
16.4%
5 5502
11.4%
0 4917
 
10.2%
9 4033
 
8.4%
1 2662
 
5.5%
2 1188
 
2.5%
7 131
 
0.3%
8 108
 
0.2%
Latin
ValueCountFrequency (%)
E 7924
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 13275
23.6%
4 8463
15.1%
E 7924
14.1%
/ 7924
14.1%
5 5502
9.8%
0 4917
 
8.7%
9 4033
 
7.2%
1 2662
 
4.7%
2 1188
 
2.1%
7 131
 
0.2%
Other values (2) 195
 
0.3%

STARTDATE
Text

Distinct529
Distinct (%)6.7%
Missing74
Missing (%)0.9%
Memory size62.5 KiB
2023-07-20T07:01:14.474705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.683870968
Min length8

Characters and Unicode

Total characters68646
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row14-Nov-04
2nd row14-Nov-04
3rd row15-Nov-04
4th row16-Nov-04
5th row17-Nov-04
ValueCountFrequency (%)
15-dec-05 44
 
0.6%
13-nov-05 39
 
0.5%
7-may-05 39
 
0.5%
25-nov-05 39
 
0.5%
1-may-05 38
 
0.5%
1-jun-05 37
 
0.5%
6-jun-05 37
 
0.5%
27-may-05 35
 
0.4%
20-aug-05 35
 
0.4%
18-may-05 35
 
0.4%
Other values (519) 7527
95.2%
2023-07-20T07:01:16.428837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15808
23.0%
0 8715
12.7%
5 6824
 
9.9%
1 3348
 
4.9%
2 3270
 
4.8%
4 2606
 
3.8%
u 2113
 
3.1%
e 1839
 
2.7%
a 1773
 
2.6%
J 1734
 
2.5%
Other values (23) 20616
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29126
42.4%
Dash Punctuation 15808
23.0%
Lowercase Letter 15808
23.0%
Uppercase Letter 7904
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2113
13.4%
e 1839
11.6%
a 1773
11.2%
c 1430
9.0%
n 1334
8.4%
p 1167
7.4%
v 1016
6.4%
o 1016
6.4%
g 900
 
5.7%
r 892
 
5.6%
Other values (4) 2328
14.7%
Decimal Number
ValueCountFrequency (%)
0 8715
29.9%
5 6824
23.4%
1 3348
 
11.5%
2 3270
 
11.2%
4 2606
 
8.9%
3 1197
 
4.1%
7 823
 
2.8%
6 797
 
2.7%
9 793
 
2.7%
8 753
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
J 1734
21.9%
A 1379
17.4%
M 1252
15.8%
N 1016
12.9%
D 746
9.4%
S 688
 
8.7%
O 684
 
8.7%
F 405
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 15808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44934
65.5%
Latin 23712
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 2113
 
8.9%
e 1839
 
7.8%
a 1773
 
7.5%
J 1734
 
7.3%
c 1430
 
6.0%
A 1379
 
5.8%
n 1334
 
5.6%
M 1252
 
5.3%
p 1167
 
4.9%
v 1016
 
4.3%
Other values (12) 8675
36.6%
Common
ValueCountFrequency (%)
- 15808
35.2%
0 8715
19.4%
5 6824
15.2%
1 3348
 
7.5%
2 3270
 
7.3%
4 2606
 
5.8%
3 1197
 
2.7%
7 823
 
1.8%
6 797
 
1.8%
9 793
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 15808
23.0%
0 8715
12.7%
5 6824
 
9.9%
1 3348
 
4.9%
2 3270
 
4.8%
4 2606
 
3.8%
u 2113
 
3.1%
e 1839
 
2.7%
a 1773
 
2.6%
J 1734
 
2.5%
Other values (23) 20616
30.0%

ENDDATE
Text

Distinct528
Distinct (%)6.7%
Missing77
Missing (%)1.0%
Memory size62.5 KiB
2023-07-20T07:01:18.220953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.683877499
Min length8

Characters and Unicode

Total characters68620
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row14-Nov-04
2nd row15-Nov-04
3rd row15-Nov-04
4th row16-Nov-04
5th row17-Nov-04
ValueCountFrequency (%)
15-dec-05 45
 
0.6%
13-nov-05 41
 
0.5%
25-nov-05 39
 
0.5%
7-may-05 39
 
0.5%
6-jun-05 39
 
0.5%
1-may-05 38
 
0.5%
14-may-05 35
 
0.4%
24-nov-05 35
 
0.4%
30-jan-05 35
 
0.4%
20-aug-05 35
 
0.4%
Other values (518) 7521
95.2%
2023-07-20T07:01:20.346947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15802
23.0%
0 8710
12.7%
5 6825
 
9.9%
1 3348
 
4.9%
2 3264
 
4.8%
4 2596
 
3.8%
u 2109
 
3.1%
e 1842
 
2.7%
a 1771
 
2.6%
J 1732
 
2.5%
Other values (23) 20621
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29115
42.4%
Dash Punctuation 15802
23.0%
Lowercase Letter 15802
23.0%
Uppercase Letter 7901
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2109
13.3%
e 1842
11.7%
a 1771
11.2%
c 1434
9.1%
n 1330
8.4%
p 1167
7.4%
o 1012
6.4%
v 1012
6.4%
g 898
 
5.7%
r 893
 
5.7%
Other values (4) 2334
14.8%
Decimal Number
ValueCountFrequency (%)
0 8710
29.9%
5 6825
23.4%
1 3348
 
11.5%
2 3264
 
11.2%
4 2596
 
8.9%
3 1207
 
4.1%
7 817
 
2.8%
9 793
 
2.7%
6 793
 
2.7%
8 762
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
J 1732
21.9%
A 1378
17.4%
M 1250
15.8%
N 1012
12.8%
D 747
9.5%
O 687
 
8.7%
S 687
 
8.7%
F 408
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 15802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44917
65.5%
Latin 23703
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 2109
 
8.9%
e 1842
 
7.8%
a 1771
 
7.5%
J 1732
 
7.3%
c 1434
 
6.0%
A 1378
 
5.8%
n 1330
 
5.6%
M 1250
 
5.3%
p 1167
 
4.9%
o 1012
 
4.3%
Other values (12) 8678
36.6%
Common
ValueCountFrequency (%)
- 15802
35.2%
0 8710
19.4%
5 6825
15.2%
1 3348
 
7.5%
2 3264
 
7.3%
4 2596
 
5.8%
3 1207
 
2.7%
7 817
 
1.8%
9 793
 
1.8%
6 793
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 15802
23.0%
0 8710
12.7%
5 6825
 
9.9%
1 3348
 
4.9%
2 3264
 
4.8%
4 2596
 
3.8%
u 2109
 
3.1%
e 1842
 
2.7%
a 1771
 
2.6%
J 1732
 
2.5%
Other values (23) 20621
30.1%

Surveyed
Text

MISSING 

Distinct39
Distinct (%)0.6%
Missing896
Missing (%)11.2%
Memory size62.5 KiB
2023-07-20T07:01:21.204952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.774954115
Min length3

Characters and Unicode

Total characters62153
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row24-Feb-05
2nd row24-Feb-05
3rd row24-Feb-05
4th row24-Feb-05
5th row24-Feb-05
ValueCountFrequency (%)
16-jan-06 786
 
11.1%
24-may-05 532
 
7.5%
25-may-05 440
 
6.2%
15-aug-05 382
 
5.4%
2-dec-04 370
 
5.2%
24-feb-05 365
 
5.2%
26-aug-05 363
 
5.1%
16-dec-05 348
 
4.9%
14-jun-05 347
 
4.9%
14-nov-05 340
 
4.8%
Other values (29) 2810
39.7%
2023-07-20T07:01:22.379001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 14142
22.8%
0 7280
11.7%
5 6601
 
10.6%
2 3142
 
5.1%
1 3055
 
4.9%
4 2785
 
4.5%
6 2381
 
3.8%
a 2087
 
3.4%
e 1947
 
3.1%
u 1801
 
2.9%
Other values (28) 16932
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26730
43.0%
Lowercase Letter 14195
22.8%
Dash Punctuation 14142
22.8%
Uppercase Letter 7086
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2087
14.7%
e 1947
13.7%
u 1801
12.7%
c 1451
10.2%
n 1438
10.1%
g 1003
7.1%
y 972
6.8%
b 730
 
5.1%
v 560
 
3.9%
o 559
 
3.9%
Other values (8) 1647
11.6%
Decimal Number
ValueCountFrequency (%)
0 7280
27.2%
5 6601
24.7%
2 3142
11.8%
1 3055
11.4%
4 2785
 
10.4%
6 2381
 
8.9%
3 699
 
2.6%
9 566
 
2.1%
8 161
 
0.6%
7 60
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
J 1591
22.5%
M 1283
18.1%
A 1214
17.1%
D 958
13.5%
F 730
10.3%
N 560
 
7.9%
O 486
 
6.9%
S 262
 
3.7%
U 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 14142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40872
65.8%
Latin 21281
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2087
 
9.8%
e 1947
 
9.1%
u 1801
 
8.5%
J 1591
 
7.5%
c 1451
 
6.8%
n 1438
 
6.8%
M 1283
 
6.0%
A 1214
 
5.7%
g 1003
 
4.7%
y 972
 
4.6%
Other values (17) 6494
30.5%
Common
ValueCountFrequency (%)
- 14142
34.6%
0 7280
17.8%
5 6601
16.2%
2 3142
 
7.7%
1 3055
 
7.5%
4 2785
 
6.8%
6 2381
 
5.8%
3 699
 
1.7%
9 566
 
1.4%
8 161
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 14142
22.8%
0 7280
11.7%
5 6601
 
10.6%
2 3142
 
5.1%
1 3055
 
4.9%
4 2785
 
4.5%
6 2381
 
3.8%
a 2087
 
3.4%
e 1947
 
3.1%
u 1801
 
2.9%
Other values (28) 16932
27.2%

Sample

Unnamed: 0HOLEIDDEPTHRLMethodEASTNORTHCollarDipCollarAzimuthRigIDTENEMENTIDSTARTDATEENDDATESurveyed
0DCB000167.0425.0RC728002.07533994.057-90.00.0DD1E45/249914-Nov-0414-Nov-0424-Feb-05
1DCB000249.0425.0RC727599.07533998.476-90.00.0DD1E45/249914-Nov-0415-Nov-0424-Feb-05
2DCB000366.0424.0RC727205.07533996.374-90.00.0DD1E45/249915-Nov-0415-Nov-0424-Feb-05
3DCB000456.0422.0RC726996.07534000.652-90.00.0DD1E45/249916-Nov-0416-Nov-0424-Feb-05
4DCB000554.0422.0RC726999.07534202.086-90.00.0DD1E45/249917-Nov-0417-Nov-0424-Feb-05
5DCB000640.0423.0RC727000.07534601.902-90.00.0DD1E45/249917-Nov-0417-Nov-0424-Feb-05
6DCB000742.0424.0RC727008.07534998.737-90.00.0DD1E45/249917-Nov-0417-Nov-0424-Feb-05
7DCB000842.0422.0RC726600.07534996.728-90.00.0DD1E45/249918-Nov-0418-Nov-0424-Feb-05
8DCB000972.0421.0RC726209.07534995.954-90.00.0DD1E45/249918-Nov-0418-Nov-0424-Feb-05
9DCB001048.0420.0RC726000.07534993.174-90.00.0DD1E45/249919-Nov-0420-Nov-0424-Feb-05